SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

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Showing 7051–7075 of 15113 papers

TitleStatusHype
Dynamic Dialogue Policy for Continual Reinforcement Learning—0
External control of a genetic toggle switch via Reinforcement Learning—0
gTLO: A Generalized and Non-linear Multi-Objective Deep Reinforcement Learning ApproachCode0
Deep Reinforcement Learning Based Semi-Autonomous Control for Robotic Surgery—0
Automatically Learning Fallback Strategies with Model-Free Reinforcement Learning in Safety-Critical Driving Scenarios—0
Exploring the Pareto front of multi-objective COVID-19 mitigation policies using reinforcement learning—0
Evaluating Vision Transformer Methods for Deep Reinforcement Learning from Pixels—0
Implementing Online Reinforcement Learning with Temporal Neural Networks—0
Settling the Sample Complexity of Model-Based Offline Reinforcement Learning—0
NeuRL: Closed-form Inverse Reinforcement Learning for Neural Decoding—0
Model-free optimization of power/efficiency tradeoffs in quantum thermal machines using reinforcement learningCode0
A Spiking Neural Network Structure Implementing Reinforcement Learning—0
Hardware Trojan Insertion Using Reinforcement Learning—0
Sim-to-Real Learning for Bipedal Locomotion Under Unsensed Dynamic Loads—0
On Improving Cross-dataset Generalization of Deepfake Detectors—0
Semantic Exploration from Language Abstractions and Pretrained Representations—0
The Complexity of Markov Equilibrium in Stochastic Games—0
Evolving Pareto-Optimal Actor-Critic Algorithms for Generalizability and Stability—0
Approximate discounting-free policy evaluation from transient and recurrent states—0
Data-Driven Evaluation of Training Action Space for Reinforcement Learning—0
Distributed Reinforcement Learning for Robot Teams: A Review—0
Imitating, Fast and Slow: Robust learning from demonstrations via decision-time planning—0
Habitat-Web: Learning Embodied Object-Search Strategies from Human Demonstrations at Scale—0
Optimizing the Long-Term Behaviour of Deep Reinforcement Learning for Pushing and Grasping—0
Q-learning with online random forests—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified